Even science is not free from myths and legends. There are some beliefs that people tend to take to heart, and data science has a few of them. These beliefs can cause problems like stereotypes that give people the wrong impression or ideas that will discourage prospective ones from pursuing the field. Here are some of the biggest myths in the sector.
1. Data Science is all Science
Data science is not just science, but an art as well. The field of data science can go beyond numbers and tables and test your reasoning ability, aptitude and creative ability. It is not a science, strictly speaking, but a combination of scientific principles and a level of artistic thinking. Each problem is a unique conundrum which cannot be solved by assigning values to a variable and solving an equation. It is not a skill to be learned but a process.
2. Data Science requires a doctorate
This assumption is not entirely correct, but a partial truth. The job role and preferences decide the level of education one needs. However, one does need a firm grasp on statistics and mathematics and fundamental coding skills. The rest depends on the type of job.
In entry-level data science jobs, such as in applied data science and analytics, you do not need a PhD. Your work requires you to use packages and algorithms built in the workplace and apply the principles for clients. However, research jobs require a PhD as you will be creating your algorithm and writing a paper on it.
3. It’s all about the tools
It is another misconception about data science. It is a field that uses various tools and applications, with computers doing most of the heavy work as far as computation goes. However, just like every other job, a device is only as good as the person using it.
The focal point of your learning should be about the practical application of the tools. Many people who enter the trade end up learning the tools only, which cannot help them succeed in this path. It is always good to know the various platforms, software and packages used in data science, but not as important as knowing when and how to use them. Therefore, the learning should be work-oriented.
4. Coding is a must-know
Coding is a versatile weapon, and knowing a programming language will do wonders for your CV no matter which career you pursue. However, It is not something to rack your brains over. Due to the widespread use of programming, ready-made codes are available on the Internet, for almost every purpose.
This is not implying that knowing coding is useless. It is always handy to know to programme, and it can also help you progress faster in the field. But not being an expert programmer must not discourage you from pursuing data sciences.
5. Predictive Modelling is Data Science
Predictive Modelling, in simple terms, is the process of using data from various sources, analysing them and establishing a possible pattern or trend that can give us an accurate picture of the future. Although this picture is not always correct, most of these predictions help find the most probable outcome. This is used in many fields, and data scientists play a crucial part in building these predictive models.
However, to categorise all of data science as predictive modelling is a terrible misconception. Yes, this is one of the more popular applications of data science, from the weather to the stock market, but it is not the whole thing.
Data Science is also about many other things, starting from data mining and data cleaning, to visualisations, anomaly detections, and yes, predictive modelling. The miracle of data science is not mere divination, but all of what happens online today.
6. AI will take over Data Science
This belief comes in different forms, from AI doing the job of humans for them to AI assimilating data science as a part of it. None of them is correct.
AI systems are capable of using Big Data to their advantage. They can do number crunching better than human beings, and have shown promise in pattern recognition and provide suggestions, targeted advertising among performing other tasks. However, there are still human beings present in that network somewhere, especially for functions such as verifying the results, maintaining the program and many more.
7. Data Scientists are rare in the market
This is not as much a misconception as it is an outdated belief. Data scientists used to be a rarity and hence was in demand. However, that situation has changed. There are more data scientists now than there was, and these are not necessarily all freshmen straight out of college. Some are experienced in their respective fields and took up lessons in different aspects of data science. This may include mathematicians, analysts, programmers, among others. In any case, they are more common nowadays. So companies realise their potential.
For a realm of science that rose from the modern age of the internet, data science sure has a lot of myths and legends around it. Some of these myths are harmless, but others are not. Believing in some of these can destroy your confidence in pursuing your dreams. As an aspiring data scientist, having the right information is key to your survival.